awesome-ai-safety VS tabby

Giskard - Testing framework for ML models| Multiple roles | Full-time | France | https://giskard.ai/

We are building the first collaborative & open-source Quality Assurance platform for all ML models - including Large Language Models.

Founded in 2021 in Paris by ex-Dataiku engineers, we are an emerging player in the fast-growing market of AI Quality & Safety.

Giskard helps Data Scientists & ML Engineering teams collaborate to evaluate, test & monitor AI models. We help organizations increase the efficiency of their AI development workflow, eliminate risks of AI biases and ensure robust, reliable & ethical AI models. Our open-source platform is used by dozens of ML teams across industries, both at enterprise companies & startups.

In 2022, we raised our first round of 1.5 million euros, led by Elaia, with participation from Bessemer and notable angel investors including the CTO of Hugging Face. To read more about this fundraising and how it will accelerate our growth, you can read this announcement: https://www.giskard.ai/knowledge/news-fundraising-2022

In 2023, we received a strategic investment from the European Commission to build a SaaS platform to automate compliance with the upcoming EU AI regulation. You can read more here: https://www.giskard.ai/knowledge/1-000-github-stars-3meu-and...

We are assembling a team of champions: Software Engineers, Machine Learning researchers, and Data Scientists ; to build our AI Quality platform and expand it to new types of AI models and industries. We have a culture of continuous learning & quality, and we help each other achieve high standards & goals!

We aim to grow from 15 to 25 people in the next 12 months. We're hiring the following roles:

Giskard - Testing framework for ML models| Multiple roles | Full-time | France | https://giskard.ai/

We are building the first collaborative & open-source Quality Assurance platform for all ML models - including Large Language Models.

Founded in 2021 in Paris by ex-Dataiku engineers, we are an emerging player in the fast-growing market of AI Safety & Security.

Giskard helps Data Scientists & ML Engineering teams collaborate to evaluate, test & monitor AI models. We help organizations increase the efficiency of their AI development workflow, eliminate risks of AI biases and ensure robust, reliable & ethical AI models. Our open-source platform is used by dozens of ML teams across industries, both at enterprise companies & startups.

In 2022, we raised our first round of 1.5 million euros, led by Elaia, with participation from Bessemer and notable angel investors including the CTO of Hugging Face. To read more about this fundraising and how it will accelerate our growth, you can read this announcement: https://www.giskard.ai/knowledge/news-fundraising-2022

In 2023, we received a strategic investment from the European Commission to build a SaaS platform to automate compliance with the upcoming EU AI regulation. You can read more here: https://www.giskard.ai/knowledge/1-000-github-stars-3meu-and...

We are assembling a team of champions: Software Engineers, Machine Learning researchers, and Data Scientists ; to build our AI Quality platform and expand it to new types of AI models and industries. We have a culture of continuous learning & quality, and we help each other achieve high standards & goals!

We aim to grow from 15 to 25 people in the next 12 months. We're hiring the following roles:

* Software Engineer - https://apply.workable.com/giskard/j/AD2C90B581/ (Python, Java, Typescript, Vue.js, Cloud skills)

* Machine Learning Researcher - https://apply.workable.com/giskard/j/E89FE8E310/ (post-PhD)

* Data Science lead - https://apply.workable.com/giskard/j/E89FE8E310/ (ML + consulting experience required)

* Growth marketing intern - https://apply.workable.com/giskard/j/C8635E9B0C/

* Data Science intern - https://apply.workable.com/giskard/j/7F0B341852/

Hi! I’ve been working on this automatic scanner for ML models to detect issues like underperforming data slices, overconfidence in predictions, robustness problems, and others. It supports all main Python ML frameworks (sklearn, torch, xgboost, …) and integrates with the quality assurance solution we are building at Giskard AI (https://giskard.ai) to systematically test models before putting them in production.

It is still a beta and I would love to hear your feedback if you have the time to try it out.

We have quite a few tutorials in the docs with ready-made colab notebooks to make it easy to get started.

If you are interested in the code:

https://github.com/Giskard-AI/giskard/tree/main/python-clien...

Repository: https://github.com/Giskard-AI/awesome-ai-safety

https://github.com/TabbyML/tabby can run self-hosted AI coding assistants. I tried it a while ago and it worked with Nvim pretty easily. There is a VS code extension too. The extension will just sort of "read" with you and provide suggestions from time to time. Anytime the suggestion is good you can press some key ( by default) to accept it. It's basically autocomplete on steroids.

NixOS just got tabbyml[1] which is built on llama-cpp. Working on systemsd services the weekend and updating latest tabbyml release which supports rocm in addition to cuda

[1] https://github.com/TabbyML/tabby

[2] https://github.com/NixOS/nixpkgs/pull/291744

Nice implementation! It should serve as a great reference for a minimal Tabby's backend API. Thank you for sharing it!

Yeah - ultimately, it won't be as performant or feature-rich compared to https://github.com/TabbyML/tabby, but it's still perfect for educational purposes!

Looks interesting! What are the main differences between this and https://github.com/TabbyML/tabby ?

TabbyML | Software Engineer (Rust) | REMOTE

Self-hosted AI coding assistant. An opensource / on-prem alternative to GitHub Copilot.

Project: https://github.com/TabbyML/tabby

Tabby is seeking a Software Engineer proficient in Rust to join our core engineering team. In this role, you will be responsible for developing the following features:

There are a bunch of VSCode extensions that make use of local models. Tabby seems to be the most friendly right now, but I admittedly haven't tried it myself: https://tabbyml.github.io/tabby/